The work is only useful if something ships.
Strategy decks gather dust. Artifacts get used. Every engagement here resolves into something concrete — an ICP map, a RACI, an output policy, an incrementality model — that a named role picks up and uses to make one specific decision. This is the catalogue of artifact types the work produces, mapped to the decision each one unblocks.
Filter 55 artifact types by category:
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A structured read of US-market readiness across product-market fit, GTM, sizing, BD, monetization, talent, and privacy posture. The 7-step diagnostic that opens most engagements.
A sequenced map of who to sell to — tiered by readiness and proof-travel speed — so the motion compounds instead of spraying every segment in parallel.
The translation of technical wins into the language a buying committee actually evaluates — reframing the category so the product reads as a budget line, not an experiment.
A target-account list with decision-maker mapping, entry points, and an outreach sequence — the first-pipeline workplan, not a CRM dump.
A peer-set read that names where not to compete, where to win, and the wedge against each category of competitor — built for displacement, not feature parity.
A focused lighthouse-account list — the smallest set of accounts whose wins travel furthest as proof — with a rationale for why each one matters.
Responsible / Accountable / Consulted / Informed assignments across the commercial decisions that keep stalling — so ownership is explicit, not assumed.
A map of which role makes — versus advises on — each class of commercial decision, with escalation thresholds. The antidote to decisions stuck in committee.
Rep productivity and territory math that ties headcount to pipeline and ACV trajectory — operator math for hiring decisions, not pitch math.
A hiring scorecard defining the outcome a role must deliver, the competencies behind it, and the first-90-day measures — so the next hire is designed, not improvised.
A phased operating plan — readiness, first pipeline, repeatable motion, scale without bloat — with a milestone gate at each stage. The cadence a retainer runs on.
The integrated system that turns founder-led selling into a repeatable motion — sales, marketing, and post-sale operating as one quota-linked function with shared definitions.
A prioritized map of candidate agentic use cases scored by value, feasibility, data readiness, and governance risk — so investment goes to workflows that can actually move from pilot to production.
A map of which decisions an agent may make autonomously, which require a human in the loop, and which are out of scope — with escalation and approval thresholds for each.
The allow-listed set of data sources and tools an agent may read, write, or call — with permissions, scopes, and audit hooks. Defines the agent's operational boundary.
The test design for whether an agent produces correct, useful, and safe outputs — task-level checks, outcome-level metrics, and the cadence for re-evaluation as inputs drift.
The guardrails for a live agent — output policy, permission limits, monitoring, and the fallback logic that defines what happens when the agent is uncertain, fails, or hits a boundary.
An end-to-end design of a single agentic workflow across its layers — signals, context, tools, decisions, governance, and outcomes — with the owner and success metric for each step.
A phased plan for moving an agentic workflow from pilot to governed production — readiness, supervised rollout, and scaled deployment — with a gate that must clear before each phase.
A vendor-neutral read of which environment fits which collaboration workflow — clean rooms, secure sharing, warehouse-native, walled gardens — by use case, not by logo.
The rules for what can leave a collaboration environment — aggregate thresholds, allowed exports, re-review triggers. The real control surface in sensitive data work.
A pre-platform map of the data footprint, governance model, sensitivity, consent, and access policy — settled before any platform is selected.
How a clean room actually runs as a workflow — templates, permitted joins, output policy, and the operating cadence that turns a one-off match study into infrastructure.
The map of metric definitions, taxonomies, IDs, and mappings that lets humans, platforms, and agents interpret data consistently — the layer usually missing until fragmentation hurts.
The checklist that turns a proof-of-concept into governed, repeatable infrastructure — use case, owner, success metric, output rules, and the path after the POC works.
A one-page map of the standards stack agentic advertising depends on — workflow protocols, runtime and signal exchange, transaction rails, privacy constraints, measurement trust, and research evidence — so a roadmap names the layers it touches instead of treating "standards" as one thing.
The transaction-rail inventory under a media or data product — OpenRTB, AdCOM, OpenDirect, Deals API, VAST, ads.txt / app-ads.txt, sellers.json, the SupplyChain Object, and the taxonomies — with the version in play and the official source to validate against.
A map of the consent and privacy signals a data flow must carry and honor — GPP / TCF strings, deletion-request propagation, and platform / OS attribution constraints — built so privacy posture is designed in, not retrofitted after legal review.
A test design that separates real, caused value from harvested demand — the causal source of truth that keeps optimization honest. Attribution is not incrementality.
The disciplined inputs a marketing-mix model needs — channel, spend, KPI, geo, time, priors, and business context — mapped before the model is run, not after.
A classification of what counts as proof for each buyer — what each network or platform metric does and does not mean — so claims travel without losing comparability.
Governed metrics and a reporting model with defined ownership — moving past dashboards that report toward a model that drives decisions and feeds the feedback loop.
A self-serve diagnostic that scores readiness across the six engagement paths and routes to the right next step — the no-email, browser-stored entry point to the work.
A repeatable structure for building proof that travels — problem, approach, what shipped, and what the buyer can replicate — designed so each win compounds the next pitch.
The recurring stalls a buying committee raises — brand safety, comparability, measurement — paired with the reframe and the proof that answers each one.
A single page that frames a board-grade decision — the market read, the options, the recommendation, and the risk — written for a board, not a sales deck.
The story the whole commercial team tells the same way — the category frame, the stakes, and the path — so positioning does not drift rep by rep.
A structure for quarterly business reviews and joint business plans that turns a renewal conversation into an expansion one — value delivered, value ahead, shared plan.
Maps how marketing signal, sales conversations, buyer objections, proof assets, pipeline movement, and customer feedback inform one revenue system.
Works backward from ARR target to pipeline, ACV, conversion rates, MQL / SQL volume, and operating cadence.
Connects intent, content engagement, sales notes, objections, customer feedback, and product usage to next-best actions.
A map of the buyer problem, the offer that solves it, what is in and out of scope, and how the offer is delivered — the structure that turns a capability into a defined, sellable product.
How the offer is split into tiers or modules a buyer can choose between — what each tier includes, who it is for, and the upgrade path between them.
The pricing model tied to the value metric the buyer actually grows on — what is charged for, how it scales, and why the metric aligns price with the value delivered.
The path that turns a successful proof-of-concept into a renewed, productized engagement — success criteria, commercial owner, conversion terms, and the steps from pilot to production revenue.
A classification of the proof each buyer in the committee needs — pilots, references, benchmarks, security, ROI — mapped to where it comes from, so the deal is not stalled on a missing receipt.
The story tuned for each member of an enterprise buying committee — economic buyer, technical buyer, user, and blocker — so a single deal lands with every role that has to say yes.
A framework for deciding which work to keep bespoke and which to productize — based on repeatability, margin, demand pattern, and delivery cost — so productization effort goes where it pays back.
A one-page ordering of evidence strength — exposure, attention, persuasion, behavior, incrementality, business value, comparability — so an agent or analyst knows which level a metric actually proves before it informs a decision.
The exposure-to-comparability stack made explicit, with the question answered and the watch-out at each level — the artifact that stops agentic optimization from collapsing seven levels of evidence into one easy metric.
A pre-citation checklist for research and measurement claims — source and recency, method and sample, proxy quality, bias checks, stated limitations, and reproducibility — before a number is used to decide or quoted externally.
The decision-threshold spec for an agentic system: which evidence lets an agent recommend, which requires human approval, and which blocks action — so budget never moves on a weak proxy at machine speed.
A side-by-side of measurement methods — attribution, incrementality tests, MMM, brand lift, attention, and BI / finance reconciliation — with what each is best for and where each misleads, so agents do not optimize correlation at machine speed.
A map of how exposure, identity, deduplication, outcome, and currency are defined across linear, streaming, CTV, digital, and retail media — so a cross-platform plan compares like with like instead of stacking incompatible definitions.
The verification guardrails on a media workflow — MRC viewability definitions, GIVT / SIVT filtration, OM SDK measurement coverage, and certification expectations — made explicit before spend or agent optimization runs against them.
The companion set to the signal-containerization essay — the artifacts that turn a raw signal into an executable, governed object an agent can discover, activate, and evaluate. Each is described as a structure, not a template, and links back to the essay that frames it.
Maps intent, semantics, provenance, policy, activation, and evaluation for an executable signal object.
Shows how a signal can activate through identity and contextual routes, including fallbacks and governance.
Defines how an agent discovers, checks, activates, monitors, and evaluates signal containers.
Documents source, method, freshness, privacy, expansion, allowed outputs, and audit rules.
Artifacts are not a menu you order from. They are the natural output of an engagement — the diagnostic produces a read, an audit produces a plan, a sprint produces pipeline, a retainer produces the operating system. Each shape tends to ship the categories below.
Self-serve diagnostic — scores the six paths and routes to the right next step.
2–3 week diagnostic across five lenses into a 90-day plan.
6–8 week execution sprint — pipeline, partner co-sell, pricing tests.
3, 6, or 12 months of senior operator partnership on a weekly cadence.
If you can name the decision that is stalling, you can name the artifact that unblocks it. Scope the set that fits your problem — or start with the scorecard if you're not sure which one.